United States Register of Introduced and Invasive Species (US-RIIS)

Introduced (non-native) species that becomes established may eventually become invasive, so tracking introduced species provides a baseline for effective modeling of species trends and interactions, geospatially and temporally. The United States Register of Introduced and Invasive Species (US-RIIS) is comprised of three lists, one each for Alaska (AK, with 532 records), Hawaii (HI, with 6,075 records), and the conterminous United States (L48, with 8,657 records). Each list includes introduced (non-native), established (reproducing) taxa that: are, or may become, invasive (harmful) in the locality; are not known to be harmful there; and/or have been used for biological control in the locality. To be included in the US-RIIS, a taxon must be non-native everywhere in the locality and established (reproducing) anywhere in the locality. Native pest species are not included. Each record has information on taxonomy, dates of introduction (where available; currently for 38%), invasion status (invasive or introduced), and citations for the authoritative sources from which this information is drawn. The US-RIIS builds on a previous dataset, A Comprehensive List of Non-Native Species Established in Three Major Regions of the U.S.: Version 3.0 (Simpson et al., 2020, https://doi.org/10.5066/p9e5k160). There are 15,264 records in the master list and 12,981 unique scientific names. The list is derived from 5,951 authoritative sources, was reviewed by or based on input from 30 invasive species scientists, and continues to be updated. Publication of version 2.0 of the US-RIIS is anticipated (but not guaranteed) in approximately 12 months. Many thanks to these additional Reviewers/Contributors: Alexander Salazar, Miami University, Ohio; Alma Solis, Smithsonian Institution; Andrew P. Landsman, National Park Service; Bethany Bradley, University of Massachusetts, Amherst; Bruce Cutler, University of Kansas; Cayla Morningstar, USGS-NAS; Chris Taliga, USDA-ARS PLANTS database; Connor Davidson Crouch, Northern Arizona University; Danielle Froelich, SWCA Environmental Consultants; Darrell Ubick, Cal Academy; Faith Campbell, Center for Invasive Species Prevention; Gerry Moore, USDA-ARS PLANTS database; Matt Bowser, US Fish and Wildlife Service; Matt Neilson, USGS-NAS; Michael Gates, USDA-ARS; Nancy Khan, Smithsonian Institution; Rachel Neville, Invasive Species Consultant; Roy Van Driesche, University of Massachusetts-Amherst; Shyama Pagad, IUCN Invasive Species Specialists Group; Terry L. Whitworth, Washington State University; Thomas Henry, USDA-ARS; Vickie Brewster, USDA-APHIS; Warren Wagner, Smithsonian Institution. Our sincere apologies to the many contributors whom we may not have mentioned. We appreciate your assistance improving the quality of this dataset.

已成功定殖的非本土物种最终可能演变为入侵物种，因此对引入物种开展追踪可为物种趋势与种间相互作用的时空地理建模提供可靠基准。
美国外来入侵物种名录（United States Register of Introduced and Invasive Species，下文简称US-RIIS）由三份名录构成，分别覆盖阿拉斯加州（AK，含532条记录）、夏威夷州（HI，含6075条记录）及美国本土48州（L48，含8657条记录）。每份名录收录满足以下任一或多项条件的引入（non-native）、已成功定殖（可自行繁殖）的分类群（taxon）：在当地已造成或可能造成入侵危害；在当地未被证实具有危害性；以及/或曾在当地被用于生物防治。
欲纳入US-RIIS的分类群，需在其分布的该区域内均为非本土物种，且至少在该区域内某一地点完成定殖。本土有害物种不予收录。每条记录包含分类学信息、引入日期（若可获取，当前仅覆盖38%的记录）、入侵状态（入侵物种或引入物种），以及支撑该数据的权威来源引文。
US-RIIS 基于此前发布的数据集《美国三大区域外来定殖物种综合名录：3.0版》（Simpson等，2020，https://doi.org/10.5066/p9e5k160）构建。该名录总记录数达15264条，包含12981个唯一科学名称。其编制依托5951份权威来源，经30位入侵物种科学家评审或吸纳其专业建言，且仍在持续更新中。US-RIIS 2.0版预计（但不保证）将在约12个月后发布。
特别感谢以下额外的评审与贡献者：俄亥俄州迈阿密大学Alexander Salazar；史密森学会Alma Solis；美国国家公园管理局Andrew P. Landsman；马萨诸塞大学阿默斯特分校Bethany Bradley；堪萨斯大学Bruce Cutler；美国地质调查局国家水生生物调查中心Cayla Morningstar；美国农业部农业研究服务局植物数据库Chris Taliga；北亚利桑那大学Connor Davidson Crouch；SWCA环境咨询公司Danielle Froelich；加州科学院Darrell Ubick；外来物种预防中心Faith Campbell；美国农业部农业研究服务局Gerry Moore；美国鱼类和野生动物管理局Matt Bowser；美国地质调查局国家水生生物调查中心Matt Neilson；美国农业部农业研究服务局Michael Gates；史密森学会Nancy Khan；入侵物种顾问Rachel Neville；马萨诸塞大学阿默斯特分校Roy Van Driesche；国际自然保护联盟入侵物种专家小组Shyama Pagad；华盛顿州立大学Terry L. Whitworth；美国农业部农业研究服务局Thomas Henry；美国农业部动植物卫生检验局Vickie Brewster；史密森学会Warren Wagner。我们对众多未被提及的贡献者致以诚挚歉意，感谢您为提升本数据集质量所提供的宝贵帮助。